To learn the complex relationships between users' attributes and sentiment gap, we employ the idea of metric learning [1]}. Metric learning is a research spot for image recognition, clustering and recommendation system [2]}, [3]}, [4]}, [5]}, [6]}. The key to metric learning is how to learn a proper set of metrics (such as Euclidean distance or other distance metrics) to represent the relationships between different entities. As a result, some different distances are explored to understand the informational data. In [7]}, the authors presented an information-theoretic approach to learn a Mahalanobis distance function for complex data. While [8]} improved this Mahalanobis distance function for use in classification tasks. To be specific, [9]} showed how to learn a Mahanalobis distance metric for k-nearest neighbor (kNN) classification by semidefinite programming. Meanwhile, some research focuses on the constraints for metric learning with large-scale data [10]}. With the predefined form of the metric matrix, metric learning can achieve a proper distance for the characterization of complex relationships.
